Department of Farm Animal Health, Utrecht University, Yalelaan 7, Utrecht 3584 CL, the Netherlands.
Department of Farm Animal Health, Utrecht University, Yalelaan 7, Utrecht 3584 CL, the Netherlands.
J Dairy Sci. 2019 May;102(5):4522-4535. doi: 10.3168/jds.2018-15327. Epub 2019 Mar 7.
Automatic milking systems (AMS) are installed on a growing number of dairy farms worldwide. Management to support good udder health might be different on farms with an AMS compared with farms milking with a conventional milking system, as risk factors for mastitis on farms using an AMS may differ. The aim of this study was to identify farm level factors associated with mastitis on Dutch dairy farms using an AMS. In 2008, risk factor data were collected using a questionnaire combined with on-farm recordings of cow, stall, and AMS hygiene on 135 farms. These risk factor data were linked to 4 udder-health-associated dependent variables: average herd somatic cell count (HeSCCav), variance of the average herd somatic cell count (SCC) on test days (HeSCCvar), the average proportion of new high SCC cases (NHiSCC), and the farmer-reported annual incidence rate of clinical mastitis (IRCM). We employed regression models using multiple imputation to deal with missing values. Due to the high dimensionality of the risk factor data, we also performed nonlinear principal component analysis (NLPCA) and regressed the dependent variables on the principal components (PC). Good hygiene of cows and of AMS were found to be related to a lower HeSCCav and less NHiSCC. Effective postmilking teat disinfection was associated with a lower NHiSCC. A higher bulk tank milk SCC threshold for farmers' action was related to more NHiSCC. Larger farm size was related to lower HeSCCvar but higher NHiSCC. Negative attitude of farmers to animal health, higher frequency of checking AMS, and more time spent on viewing computer data were all positively related to higher IRCM. An NLPCA with 3 PC explained 16.3% of the variance in the risk factor variables. Only the first 2 PC were associated with mastitis. The first PC reflected older and larger farms with poor cow hygiene and AMS hygiene, and was related to higher HeSCCav and NHiSCC, whereas the second PC reflected newly built smaller farms with poor cow hygiene and low milk production, and was associated with higher HeSCCvar and NHiSCC, but lower IRCM. Our study suggests that many of the risk factors on conventional milking system farms are applicable to AMS farms, specifically concerning hygiene of the cows and the milking machine, but on large AMS farms, udder health may need more attention than on smaller AMS farms. Multiple imputation is instrumental to deal with missing values and NLPCA is a useful technique to process high dimensional data in our study.
自动挤奶系统（AMS）在全球越来越多的奶牛场安装。与使用传统挤奶系统的奶牛场相比，管理方式可能会有所不同，以支持良好的乳房健康，因为使用 AMS 的奶牛场的乳腺炎风险因素可能不同。本研究的目的是确定与荷兰使用 AMS 的奶牛场乳腺炎相关的农场水平因素。2008 年，使用结合了奶牛、牛舍和 AMS 卫生记录的问卷收集了风险因素数据，共 135 个农场。这些风险因素数据与 4 个与乳房健康相关的依赖变量相关联：平均牛群体细胞计数（HeSCCav）、检测日平均牛群体细胞计数（SCC）的方差（HeSCCvar）、新的高 SCC 病例的平均比例（NHiSCC）和农民报告的临床乳腺炎年度发病率（IRCM）。我们使用多元插补来处理缺失值的回归模型。由于风险因素数据的高维度，我们还进行了非线性主成分分析（NLPCA），并将因变量回归到主成分（PC）上。良好的奶牛和 AMS 卫生与较低的 HeSCCav 和较少的 NHiSCC 有关。有效的挤奶后乳头消毒与较低的 NHiSCC 有关。农民的牛奶体细胞计数阈值较高，与更多的 NHiSCC 有关。较大的农场规模与较低的 HeSCCvar 有关，但与较高的 NHiSCC 有关。农民对动物健康的消极态度、检查 AMS 的频率较高以及花费更多时间查看计算机数据与较高的 IRCM 呈正相关。解释风险因素变量方差的 16.3%的 NLPCA 具有 3 个 PC。只有前 2 个 PC 与乳腺炎有关。第一个 PC 反映了较老和较大的农场，奶牛卫生和 AMS 卫生较差，与较高的 HeSCCav 和 NHiSCC 有关，而第二个 PC 反映了新建的较小农场，奶牛卫生和产奶量较低，与较高的 HeSCCvar 和 NHiSCC 有关，但较低的 IRCM。我们的研究表明，传统挤奶系统农场的许多风险因素也适用于 AMS 农场，特别是与奶牛和挤奶机的卫生有关，但在大型 AMS 农场，乳房健康可能需要比小型 AMS 农场更多的关注。多元插补是处理缺失值的有效方法，NLPCA 是处理本研究中高维数据的有用技术。
